


import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				lowerCAmelCase       		=    False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=3				,     lowercase__=1_8				,     lowercase__=3_0				,     lowercase__=4_0_0				,     lowercase__=None				,     lowercase__=True				,     lowercase__=True				,     lowercase__=None				,     ):
							__UpperCAmelCase			:							Any	    =	size if size is not None else {'height': 2_0, 'width': 2_0}
							__UpperCAmelCase			:							Any	    =	parent
							__UpperCAmelCase			:							Any	    =	batch_size
							__UpperCAmelCase			:							Tuple	    =	num_channels
							__UpperCAmelCase			:							Optional[int]	    =	image_size
							__UpperCAmelCase			:							Any	    =	min_resolution
							__UpperCAmelCase			:							str	    =	max_resolution
							__UpperCAmelCase			:							Optional[int]	    =	size
							__UpperCAmelCase			:							Optional[Any]	    =	do_normalize
							__UpperCAmelCase			:							Tuple	    =	do_convert_rgb
							__UpperCAmelCase			:							Tuple	    =	[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
							__UpperCAmelCase			:							List[str]	    =	patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}


	def  A(    self):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
							__UpperCAmelCase			:							List[str]	    =	Image.open(requests.get(lowercase__				,     stream=lowercase__).raw).convert('''RGB''')
							return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''							,						)
@require_torch
@require_vision
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Any										=						PixaStructImageProcessor if is_vision_available() else None


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	PixaStructImageProcessingTester(self)


	@property
	def  A(    self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__				,     '''do_normalize'''))
							self.assertTrue(hasattr(lowercase__				,     '''do_convert_rgb'''))


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.image_processor_tester.prepare_dummy_image()

							__UpperCAmelCase			:							Union[str, Any]	    =	self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase			:							Optional[int]	    =	2_0_4_8

							__UpperCAmelCase			:							int	    =	image_processor(lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,     torch.tensor(0.0_6_0_6)				,     atol=1e-3				,     rtol=1e-3))


	def  A(    self):
							# Initialize image_processor
							__UpperCAmelCase			:							List[str]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							__UpperCAmelCase			:							int	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     Image.Image)

							# Test not batched input
							__UpperCAmelCase			:							str	    =	(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													__UpperCAmelCase			:							int	    =	image_processor(
													    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (1, max_patch, expected_hidden_dim)				,     )

													# Test batched
													__UpperCAmelCase			:							Optional[int]	    =	image_processor(
													    lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,     )


	def  A(    self):
							# Initialize image_processor
							__UpperCAmelCase			:							Optional[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							__UpperCAmelCase			:							str	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     Image.Image)

							# Test not batched input
							__UpperCAmelCase			:							List[str]	    =	(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							__UpperCAmelCase			:							int	    =	True

							for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													with self.assertRaises(lowercase__):
																			__UpperCAmelCase			:							int	    =	image_processor(
																			    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches

													__UpperCAmelCase			:							List[str]	    =	'Hello'

													__UpperCAmelCase			:							Any	    =	image_processor(
													    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__				,     header_text=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (1, max_patch, expected_hidden_dim)				,     )

													# Test batched
													__UpperCAmelCase			:							Union[str, Any]	    =	image_processor(
													    lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__				,     header_text=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,     )


	def  A(    self):
							# Initialize image_processor
							__UpperCAmelCase			:							List[str]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							__UpperCAmelCase			:							Optional[Any]	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     numpify=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     np.ndarray)

							__UpperCAmelCase			:							Dict	    =	(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													__UpperCAmelCase			:							str	    =	image_processor(
													    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (1, max_patch, expected_hidden_dim)				,     )

													# Test batched
													__UpperCAmelCase			:							Union[str, Any]	    =	image_processor(
													    lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,     )


	def  A(    self):
							# Initialize image_processor
							__UpperCAmelCase			:							List[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							__UpperCAmelCase			:							Any	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__				,     torchify=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     torch.Tensor)

							# Test not batched input
							__UpperCAmelCase			:							str	    =	(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													__UpperCAmelCase			:							Tuple	    =	image_processor(
													    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (1, max_patch, expected_hidden_dim)				,     )

													# Test batched
													__UpperCAmelCase			:							Dict	    =	image_processor(
													    lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,     )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''							,						)
@require_torch
@require_vision
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		int										=						PixaStructImageProcessor if is_vision_available() else None


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	PixaStructImageProcessingTester(self				,     num_channels=4)
							__UpperCAmelCase			:							int	    =	3


	@property
	def  A(    self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(lowercase__				,     '''do_normalize'''))
							self.assertTrue(hasattr(lowercase__				,     '''do_convert_rgb'''))


	def  A(    self):
							# Initialize image_processor
							__UpperCAmelCase			:							List[str]	    =	self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							__UpperCAmelCase			:							List[str]	    =	prepare_image_inputs(self.image_processor_tester				,     equal_resolution=lowercase__)
							for image in image_inputs:
													self.assertIsInstance(lowercase__				,     Image.Image)

							# Test not batched input
							__UpperCAmelCase			:							Optional[Any]	    =	(
							    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													__UpperCAmelCase			:							List[Any]	    =	image_processor(
													    image_inputs[0]				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (1, max_patch, expected_hidden_dim)				,     )

													# Test batched
													__UpperCAmelCase			:							Optional[int]	    =	image_processor(
													    lowercase__				,     return_tensors='''pt'''				,     max_patches=lowercase__).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,     )









"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]   )	->       List[str]:
											lowerCAmelCase   :Dict						=			str(id_   )
											lowerCAmelCase   :str						=			None
											lowerCAmelCase   :Any						=			None
											lowerCAmelCase   :List[str]						=			[]
											lowerCAmelCase   :List[Any]						=			{}  # {vertex:distance}
					def __lt__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Any   )	->       str:
											return self.key < other.key
					def __repr__(							self							:	Union[str, Any]   )	->       Dict:
											return self.id
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Tuple   )	->       List[str]:
											self.neighbors.append(UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	List[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			weight


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						graph[a - 1].add_neighbor(graph[b - 1] )
						graph[b - 1].add_neighbor(graph[a - 1] )
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]     ,      a__ )
						graph[b - 1].add_edge(graph[a - 1]     ,      a__ )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			[]
						for u in graph:
												lowerCAmelCase   :Tuple						=			math.inf
												lowerCAmelCase   :Any						=			None
						lowerCAmelCase   :Dict						=			0
						lowerCAmelCase   :int						=			graph[:]
						while q:
												lowerCAmelCase   :str						=			min(a__ )
												q.remove(a__ )
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :List[str]						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
						for i in range(1     ,      len(a__ ) ):
												a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
						return a


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						for u in graph:
												lowerCAmelCase   :List[str]						=			math.inf
												lowerCAmelCase   :str						=			None
						lowerCAmelCase   :Optional[int]						=			0

						lowerCAmelCase   :Dict						=			list(a__ )
						hq.heapify(a__ )

						while h:
												lowerCAmelCase   :Union[str, Any]						=			hq.heappop(a__ )
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :int						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
																								hq.heapify(a__ )

						for i in range(1     ,      len(a__ ) ):
												yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def        UpperCAmelCase      (					):





						'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()